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Cognition

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Predicting Low Cognitive Ability at Age 5-Feature Selection Using Machine Learning Methods and Birth Cohort Data.

International journal of public health
In this study, we applied the random forest (RF) algorithm to birth-cohort data to train a model to predict low cognitive ability at 5 years of age and to identify the important predictive features. Data was from 1,070 participants in the Irish pop...

Capturing advanced human cognitive abilities with deep neural networks.

Trends in cognitive sciences
How can artificial neural networks capture the advanced cognitive abilities of pioneering scientists? I suggest they must learn to exploit human-invented tools of thought and human-like ways of using them, and must engage in explicit goal-directed pr...

ognitive utcomes in the ragmatic nvestigation of optimaxygen argets (CO-PILOT) trial: protocol and statistical analysis plan.

BMJ open
INTRODUCTION: Long-term cognitive impairment is one of the most common complications of critical illness among survivors who receive mechanical ventilation. Recommended oxygen targets during mechanical ventilation vary among international guidelines....

LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the bioplausible neuronal dynamics and simplicity, LIF-SNN benefits from event...

Embedding cognitive framework with self-attention for interpretable knowledge tracing.

Scientific reports
Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to...

Multi-Aspect enhanced Graph Neural Networks for recommendation.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have achieved remarkable performance in personalized recommendation, for their powerful data representation capabilities. However, these methods still face several challenging problems: (1) the majority of user-item inter...

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data.

Methods of information in medicine
BACKGROUND: Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputa...

Degrees of algorithmic equivalence between the brain and its DNN models.

Trends in cognitive sciences
Deep neural networks (DNNs) have become powerful and increasingly ubiquitous tools to model human cognition, and often produce similar behaviors. For example, with their hierarchical, brain-inspired organization of computations, DNNs apparently categ...

A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network.

Computational intelligence and neuroscience
The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of t...

A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis-A Preliminary Statistical Investigation of the Bean Counter Profiling Scale Robustness.

International journal of environmental research and public health
A bean counter is defined as an accountant or economist who makes financial decisions for a company or government, especially someone who wants to severely limit the amount of money spent. The rise of the bean counter in both public and private compa...